Traditional methods of image resizing involve scaling an image without regard to its content. Reducing the size of an image using such methods oftentimes renders objects in the image indistinguishable. As a result, techniques have been developed to retain contextually important features of an image during resizing, while disregarding less important features of an image.
One such technique utilizes seam operations to identify contextually less important features in an image during a resizing operation. Avidan, Shai and Shamir, Ariel, Seam Carving for Content-Axare Image Resizinq, SIGGRAPH'07 Session Image Slicing & Stretching, 2007, describes seaming operations in which low-energy seams are defined and removed to reduce an image size. In Avidan, a seam is defined as an 8-connected curve extending vertically, from top to bottom, or extending horizontally, from the left side to the right side of the image. A pixel-wise energy map is pre-calculated, where energy is defined as the gradient intensity or as other energy measures. The lowest energy curves in the horizontal and/or vertical directions are determined by dynamic programming. To reduce the size of the image, the lowest energy curves are removed, as critical image contents are unlikely to be presented in low energy sections of the image. Enlarging the size of the images can be similarly performed by seam insertion.
Seam operations described in Avidan achieve content awareness, as the critical objects or scenes will be maintained while the image resized. Further, because the seams extend across height and/or width of the image, the image size is uniformly changed during resizing operations.
However, while known seam operations maintain content awareness, they introduce discontinuity or abruptness as seams are increasingly removed or added for image resizing. Inevitably, higher energy seams are removed while an image is progressively reduced, resulting in loss of content details. Similarly, while an image is enlarged, added seams do not adequately include content details that effectively correlate with surrounding image features.